code: def test_target_encoder_fit_transform_HaveCategoricalColumn_ExpectCorrectValueInResult(
      self,
  ):
      k = 2
      f = 10
      binary_cat_example = pd.DataFrame(
          {
              "Trend": pd.Categorical(
                  [
                      "UP",
                      "UP",
                      "DOWN",
                      "FLAT",
                      "DOWN",
                      "UP",
                      "DOWN",
                      "FLAT",
                      "FLAT",
                      "FLAT",
                  ],
                  categories=["UP", "FLAT", "DOWN"],
              ),
              "target": [1, 1, 0, 0, 1, 0, 0, 0, 1, 1],
          }
      )
      encoder = encoders.TargetEncoder(
          cols=["Trend"], min_samples_leaf=k, smoothing=f
      )
      result = encoder.fit_transform(
          binary_cat_example, binary_cat_example["target"]
      )
      values = result["Trend"].array
      self.assertAlmostEqual(0.5874, values[0], delta=1e-4)
      self.assertAlmostEqual(0.5874, values[1], delta=1e-4)
      self.assertAlmostEqual(0.4125, values[2], delta=1e-4)
      self.assertEqual(0.5, values[3])
  
imports: import pandas
  from unittest import TestCase
  import tests.helpers
  import numpy
  from category_encoders.datasets import load_postcodes, load_compass
  import category_encoders
label_desc: Use image node_id 5 for calling the TestTargetEncoder obj's underlying member method code with example usage: obj.test_target_encoder_fit_transform_HaveCategoricalColumn_ExpectCorrectValueInResult() without return types